Performance enhancement of Battery Management System using Unscented Kalman Filter Approach

In the era of the vanishing of conventional energy sources, battery technology has gained tremendous demand. Thus, for the safe operation and the optimized utilization of the battery, it's modelling, and the state estimation is essential. However, in real-time situations, the accurate estimation of battery is quite hard and challenging because of its nonlinear characteristic and the influence of the various factors like driving load characteristics and operational conditions on battery performance. The state of charge (SOC) is a key parameter in the battery which gives an amount of the energy stored in the battery and it depends on various external factors like aging, temperature, and charging-discharging rate of the battery. In the literature, various methods exist for SOC estimation, however, these methods fail to consider the effect of external parameters. In view of this, the paper proposes a method in which the effect of the temperature is considered in SOC estimation by re-framing the existing state space battery model. This revised battery model is obtained by considering a temperature coefficient which illustrates the relation between SOC and the temperature. For the SOC estimation, an unscented Kalman filter (UKF) approach is used. Furthermore, the proposed method considers the influence of external factors as well as nonlinear characteristics of the battery. Finally, the proposed methodology is validated in MATLAB, for different temperature scenarios and the results shows the impact of temperature variation on SOC estimation.